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Explicit Nation-Wide Habitat Models for Common Italian Piciformes

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Explicit Nation-Wide Habitat Models for Common Italian Piciformes Avocetta 33: 115-122 (2009) Explicit nation-wide habitat models for common Italian Piciformes Tommaso Campedelli2, GuGlielmo londi2, lorenzo mini2, Guido Tellini Florenzano1,2 1 MITO2000, National Committee 2 DREAM Italia - Via Garibaldi 3, I-52015 Pratovecchio (AR), Italy ([email protected]) Abstract – The aim of this paper is to assess the effect of broad scale environmental and geographical variables on the presence of Italian commonest breeding Piciformes (Wryneck, Green, Black, and Great Spotted Woodpeckers). Using the data of MITO2000 (2000-2003), logistic regression models were built, relating their presence with area occupied by Corine land-use categories, habitat diversity, morpho- logical and elevation-range, latitude and longitude. Our analyses seem to give some basic information about large scale ecology of the species, allowing to indicate different habitat types (heterogeneus habitat for Wryneck, broadleaved woods for Green Woodpecker, woods for Great Spotted Woodpecker). Our models clearly show the importance of geographical variables to infer how species ecology chang- es at different latitudes: Green and Great Spotted Woodpecker for example, become commoner, and less linked to woods, northwards. INTRODUCTION 2003, Brotons et al. 2004a, Rushton et al. 2004). Although some doubts on their effectiveness still remain (Gutzwiller Woodpeckers are typically considered forest species and and Barrow 2003, Chamberlain et al. 2004), these tech- efficient woodland biodiversity indicators, from local niques, if correctly applied, have proved to be valuable (Mikusinski et al. 2001) to continental scale (Mikusinski tools for the knowledge of the actual distribution of spe- and Angelstam 1998). Moreover, the ecology of wood- cies and to obtain objective information on the main eco- peckers is complex: they show a different degree of spe- logical factors affecting their presence (Guisan and Zim- cialization (Angelstam and Mikusinski 1994) and, even merman 2000, Scott et al. 2002, Bustamante and Seoane if they need tree-presence anyway, some species have 2004). Ecological models built at regional scale have re- evolved to live in non forested habitats (Gorman 2004). sulted able to stress some features about woodpecker ecol- In Italy nine woodpecker species are regularly present: ogy, particularly to what concern habitat preferences (To- Picus canus and Picoides tridactylus have a distribution balske and Tobalske 1999). limited to the north-east part of Italy; Dendrocopos me- The availability of ornithological data originating from dius and Dendrocopos leucotos are concentrated in small national monitoring schemes, that may be related to broad areas in the central and southern Apennines only; Dendro- scale variables, enables to build habitat suitability models copos minor, even though it is widespread along continen- with great geographical value by means of GIS applica- tal and peninsular regions, results scarcely detected due tion (Scott et al. 2002, Brotons et al. 2004a, Suarez-Seoane to its low detectability (Meschini and Frugis 1993). The et al. 2004). Due to their objectivity, these models can be most widespread species are Jynx torquilla, Picus viridis, used as reliable tools supporting the decisions for biodiver- Dryocopus martius and Dendrocopos major, these species sity conservation, allowing planners to know more on the are less demanding in terms of habitat requirements, be- factors affecting species distributions and projecting those ing, for example, less linked with the presence of mature beyond sampled areas (Naesset 1997, Woodhouse et al. stands and dead wood (Mikusinski and Angelstam 1998, 2000, Osborne 2005). Gorman 2004). Apart from information describing their habitat (e.g. Mathematical models are useful tools to describe spe- Pedrini et al. 2005), the ecological requirements of Ital- cies ecology and they are widely used in ornithology (Os- ian woodpeckers are reported only for few species at lo- borne et al. 2001, Ambrosini et al. 2002, Seoane et al. cal scale (e.g. Tellini Florenzano 1996, Bettiol et al. 2000, © 2009 CISO - Centro Italiano Studi Ornitologici 115 Campedelli et al. Rassati et al. 2001, Ceruti et al. 2003, Guerrieri and Cast- sidering the characteristics of our sampling design, in or- aldi 2003, Pirovano et al. 2003). The only attempt of eco- der to limit this problem we have excluded all grid cells logical analysis at national scale concerns the definition of with only one point count, considering each species absent deterministic models for Italian vertebrates (Boitani et al. in every grid cell in which at least two point counts were 2002), based on subjective evaluation by specialists. carried out without finding it. Models have been built us- Since a national synthesis based on field observa- ing logistic regression (software Statistica 7.0) because, at tion data is missing and due to the availability of large the time when the analyses were processed (autumn 2006), amount of information on breeding woodpecker localiza- presence-only methods were not yet available. tions in Italian territory that comes from the database of The environmental and geographical variables used to MITO2000 project (Fornasari et al. 2004), we have calcu- build the models are reported in Tab. 1. Besides the clas- lated stochastic models at the national scale for the occur- sic geographical variables (e.g., longitude), we individuat- rence of the four Italian commonest Piciformes (J. torquil- ed some areas characterised by biogeographical specificity la, P. viridis, D. martius and D. maior) assessing the effect (i.e., river Po flood plain, Sicily, Sardinia, see Minelli et of environmental (land use, but also ground morphology) al. 2002) that were analysed as “dummy” variables (Waite and geographical (e.g. latitude) variables. In particular, we 2000). In each 2.5 x 2.5 km mesh, we calculated the me- aimed to assess the effect of large scale environmental var- dian altitude using the data obtained from a DTM (Digital iables (land use and topography) and geographical ones, Terrain Model; cell size 300 m2). Standard deviation of al- whose effects can be likely ascribed to changes in climatic titude was used to describe the topography (as altitude is conditions and seem to play a decisive role in explaining more variable as the morphology is more uneven). For the the distribution of the investigated species, greatly improv- environmental variables we used the Corine Land Cover ing the validity of the models (Hawkins and Diniz-Filho map (third level; Büttner et al. 1998) retrieving the surface 2004). These variables, largely used in the past to describe of different land use in each 2.5 x 2.5 km mesh. As a meas- the ecogeography of birds (e.g. Voous 1960), were subse- ure of environmental diversity, we calculated the Shannon quently scarcely taken into account since their effect was index on land-use Corine categories. considered difficult to prove (see Fasola 1985). In recent Interpolating point-abundance data (IDW method; see years, due to the aid of more reliable and robust statistical Fortin and Dale 2005), we defined the range of each spe- techniques, variables such as latitude are newly considered cies assuming as its boundaries the isoline that excludes of capital importance to define the ecology of bird species, less than 1% of species presence. Subsequent analyses at least at large scale (e.g. Forsman and Mönkkönen 2003). were conducted only on the meshes included in this range. The IDW method (Inverse Distance Weighting) calculates a value for each grid cell from data points within a speci- STUDY AREA AND METHODS fied search radius, weighting closer point more than those farther away. We analysed the presence/absence data look- We overlapped a 2.5 x 2.5 km grid on the whole Italian ing for the effects of first and second polynomial order for territory excluding small islands. The grid mesh was em- every variable and their possible interactions (Draper and pirically selected considering the structure of the available Smith, 1998). To obtain the most informative models of database and the ecology of the investigated species. For descriptive nature (McNally 2000), they were built select- each grid mesh, we defined the presence or absence of each ing the best combination of variables according to the AIC investigated species and we calculated the values of sev- (Akaike Information Criterion, McQuarrie and Tsai 1998) eral environmental and geographical variables. value. Models with lower AIC indicate a greater degree The data on bird occurrence were obtained from MI- of parsimony (greater variance explained per parameter). TO2000 database, the Italian Bird Monitoring Scheme, Then we measured the efficiency of the obtained models that consists of randomly selected 10-min point counts selecting, for each species, the most informative one based (Fornasari et al. 2002). We took into account data collect- on the AUC value (Area Under the ROC Curve, Hosmer ed in 2000-2003 years for the four Italian commonest Pici- and Lemeshow 2000). In all cases, we included in the mod- formes species, i.e. J. torquilla, P. viridis, D. martius, D. els only the variables, or their interactions, whose marginal major. contribution was significant (p<0.05). Each species was considered present in every grid cell Only for J. torquilla, P. viridis and D. major, due to in which it was recorded at least one time; to what con- the big amount of available data, we firstly built the mod- cern the absence, due to the well known problems in cop- el with a data subset, randomly chosen and corresponding ing with false-absence cases (Kerr et al. 2000), and con- to the 80% of the available ones, and then we validated it 116 Explicit nation-wide habitat models for common Italian Piciformes (checking for their statistical significance, P<0.05) using parameter, either used alone or considering the cumulative the remaining 20% (Massolo and Meriggi 1995); the defin- effect produced by the interaction with other variables. In itive model was built using all available data.
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